Overview

Dataset statistics

Number of variables20
Number of observations42307
Missing cells1771
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical5
DateTime2
Text3

Alerts

LowDoc is highly imbalanced (63.8%)Imbalance
MIS_Status is highly imbalanced (50.8%)Imbalance
RevLineCr has 1079 (2.6%) missing valuesMissing
LowDoc has 531 (1.3%) missing valuesMissing
NoEmp has 2994 (7.1%) zerosZeros
CreateJob has 28889 (68.3%) zerosZeros
RetainedJob has 26056 (61.6%) zerosZeros
FranchiseCode has 26392 (62.4%) zerosZeros
Sector has 9798 (23.2%) zerosZeros

Reproduction

Analysis started2024-02-06 11:48:25.041720
Analysis finished2024-02-06 11:48:50.408340
Duration25.37 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Term
Real number (ℝ)

Distinct228
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.60167
Minimum0
Maximum360
Zeros94
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:50.516120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q156
median82
Q3168
95-th percentile293
Maximum360
Range360
Interquartile range (IQR)112

Descriptive statistics

Standard deviation84.569847
Coefficient of variation (CV)0.77871587
Kurtosis-0.28186028
Mean108.60167
Median Absolute Deviation (MAD)30
Skewness1.0246757
Sum4594611
Variance7152.0589
MonotonicityNot monotonic
2024-02-06T20:48:50.691431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3649
 
8.6%
83 2863
 
6.8%
84 1546
 
3.7%
57 1325
 
3.1%
81 1300
 
3.1%
58 1295
 
3.1%
59 1161
 
2.7%
56 1046
 
2.5%
240 892
 
2.1%
241 875
 
2.1%
Other values (218) 26355
62.3%
ValueCountFrequency (%)
0 94
0.2%
1 54
 
0.1%
2 75
 
0.2%
3 62
 
0.1%
4 91
 
0.2%
5 140
0.3%
6 145
0.3%
7 158
0.4%
8 188
0.4%
9 232
0.5%
ValueCountFrequency (%)
360 1
 
< 0.1%
325 7
 
< 0.1%
312 11
 
< 0.1%
311 22
 
0.1%
310 19
 
< 0.1%
309 45
 
0.1%
308 69
0.2%
306 72
0.2%
303 122
0.3%
302 130
0.3%

NoEmp
Real number (ℝ)

ZEROS 

Distinct196
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7043043
Minimum0
Maximum202
Zeros2994
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:50.855405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q312
95-th percentile40
Maximum202
Range202
Interquartile range (IQR)10

Descriptive statistics

Standard deviation17.488022
Coefficient of variation (CV)1.8020892
Kurtosis40.742751
Mean9.7043043
Median Absolute Deviation (MAD)3
Skewness5.4942159
Sum410560
Variance305.83092
MonotonicityNot monotonic
2024-02-06T20:48:51.029031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5563
13.1%
2 5518
13.0%
4 4902
11.6%
1 4433
 
10.5%
5 3146
 
7.4%
0 2994
 
7.1%
6 1691
 
4.0%
15 975
 
2.3%
16 914
 
2.2%
7 901
 
2.1%
Other values (186) 11270
26.6%
ValueCountFrequency (%)
0 2994
7.1%
1 4433
10.5%
2 5518
13.0%
3 5563
13.1%
4 4902
11.6%
5 3146
7.4%
6 1691
 
4.0%
7 901
 
2.1%
8 602
 
1.4%
9 544
 
1.3%
ValueCountFrequency (%)
202 1
 
< 0.1%
198 1
 
< 0.1%
197 2
< 0.1%
195 1
 
< 0.1%
194 2
< 0.1%
193 2
< 0.1%
192 2
< 0.1%
191 3
< 0.1%
189 3
< 0.1%
188 2
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
1.0
33405 
2.0
8902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126921
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Length

2024-02-06T20:48:51.179731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T20:48:51.301612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Most occurring characters

ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84614
66.7%
Other Punctuation 42307
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42307
50.0%
1 33405
39.5%
2 8902
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

CreateJob
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1837285
Minimum0
Maximum70
Zeros28889
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:51.594220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum70
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.0939801
Coefficient of variation (CV)2.3326985
Kurtosis23.628207
Mean2.1837285
Median Absolute Deviation (MAD)0
Skewness4.0134768
Sum92387
Variance25.948633
MonotonicityNot monotonic
2024-02-06T20:48:51.759375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 28889
68.3%
3 3293
 
7.8%
1 2519
 
6.0%
4 1334
 
3.2%
8 665
 
1.6%
9 579
 
1.4%
2 548
 
1.3%
10 511
 
1.2%
7 484
 
1.1%
11 448
 
1.1%
Other values (39) 3037
 
7.2%
ValueCountFrequency (%)
0 28889
68.3%
1 2519
 
6.0%
2 548
 
1.3%
3 3293
 
7.8%
4 1334
 
3.2%
5 73
 
0.2%
6 250
 
0.6%
7 484
 
1.1%
8 665
 
1.6%
9 579
 
1.4%
ValueCountFrequency (%)
70 1
 
< 0.1%
60 3
 
< 0.1%
57 5
 
< 0.1%
56 5
 
< 0.1%
50 6
 
< 0.1%
48 12
< 0.1%
47 15
< 0.1%
46 19
< 0.1%
45 16
< 0.1%
40 17
< 0.1%

RetainedJob
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4781478
Minimum0
Maximum140
Zeros26056
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:51.921433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile15
Maximum140
Range140
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1136484
Coefficient of variation (CV)2.3327497
Kurtosis40.299963
Mean3.4781478
Median Absolute Deviation (MAD)0
Skewness5.1823038
Sum147150
Variance65.83129
MonotonicityNot monotonic
2024-02-06T20:48:52.086146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
8 1227
 
2.9%
9 1060
 
2.5%
3 1057
 
2.5%
7 986
 
2.3%
10 837
 
2.0%
2 803
 
1.9%
11 795
 
1.9%
12 769
 
1.8%
Other values (73) 4871
 
11.5%
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
2 803
 
1.9%
3 1057
 
2.5%
4 687
 
1.6%
5 329
 
0.8%
6 575
 
1.4%
7 986
 
2.3%
8 1227
 
2.9%
9 1060
 
2.5%
ValueCountFrequency (%)
140 1
 
< 0.1%
136 1
 
< 0.1%
130 1
 
< 0.1%
118 1
 
< 0.1%
102 2
 
< 0.1%
100 4
< 0.1%
95 4
< 0.1%
91 7
< 0.1%
90 6
< 0.1%
87 7
< 0.1%

FranchiseCode
Real number (ℝ)

ZEROS 

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1955.056
Minimum0
Maximum90709
Zeros26392
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:52.251724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum90709
Range90709
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10541.389
Coefficient of variation (CV)5.3918602
Kurtosis35.690306
Mean1955.056
Median Absolute Deviation (MAD)0
Skewness5.919311
Sum82712555
Variance1.1112088 × 108
MonotonicityNot monotonic
2024-02-06T20:48:52.412110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
27760 21
 
< 0.1%
74750 20
 
< 0.1%
72590 18
 
< 0.1%
73000 18
 
< 0.1%
73675 15
 
< 0.1%
34850 15
 
< 0.1%
36680 14
 
< 0.1%
Other values (261) 1579
 
3.7%
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
5725 1
 
< 0.1%
6410 1
 
< 0.1%
9120 1
 
< 0.1%
9450 1
 
< 0.1%
10482 1
 
< 0.1%
10494 1
 
< 0.1%
10528 3
 
< 0.1%
ValueCountFrequency (%)
90709 1
 
< 0.1%
89769 2
< 0.1%
89655 1
 
< 0.1%
89640 2
< 0.1%
89352 1
 
< 0.1%
89350 1
 
< 0.1%
88875 2
< 0.1%
88355 1
 
< 0.1%
87350 3
< 0.1%
86720 3
< 0.1%

RevLineCr
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1079
Missing (%)2.6%
Memory size661.0 KiB
N
27618 
Y
7353 
0
5561 
T
 
696

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41228
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd row0
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 27618
65.3%
Y 7353
 
17.4%
0 5561
 
13.1%
T 696
 
1.6%
(Missing) 1079
 
2.6%

Length

2024-02-06T20:48:52.554205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T20:48:52.674555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
n 27618
67.0%
y 7353
 
17.8%
0 5561
 
13.5%
t 696
 
1.7%

Most occurring characters

ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35667
86.5%
Decimal Number 5561
 
13.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 5561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35667
86.5%
Common 5561
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Common
ValueCountFrequency (%)
0 5561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

LowDoc
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing531
Missing (%)1.3%
Memory size661.0 KiB
N
34313 
Y
5277 
0
 
684
A
 
570
S
 
540

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41776
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 34313
81.1%
Y 5277
 
12.5%
0 684
 
1.6%
A 570
 
1.3%
S 540
 
1.3%
C 392
 
0.9%
(Missing) 531
 
1.3%

Length

2024-02-06T20:48:52.806882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T20:48:52.935051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
n 34313
82.1%
y 5277
 
12.6%
0 684
 
1.6%
a 570
 
1.4%
s 540
 
1.3%
c 392
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41092
98.4%
Decimal Number 684
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41092
98.4%
Common 684
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Common
ValueCountFrequency (%)
0 684
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%
Distinct916
Distinct (%)2.2%
Missing150
Missing (%)0.4%
Memory size661.0 KiB
Minimum1977-06-14 00:00:00
Maximum2073-12-06 00:00:00
2024-02-06T20:48:53.089000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:53.258363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MIS_Status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
1
37767 
0
4540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Length

2024-02-06T20:48:53.405246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T20:48:53.521430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring characters

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Sector
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.933439
Minimum0
Maximum81
Zeros9798
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:53.643458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median33
Q344
95-th percentile72
Maximum81
Range81
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.291386
Coefficient of variation (CV)0.67686178
Kurtosis-0.89972227
Mean32.933439
Median Absolute Deviation (MAD)11
Skewness-0.11631806
Sum1393315
Variance496.90589
MonotonicityNot monotonic
2024-02-06T20:48:53.792302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9798
23.2%
42 7337
17.3%
33 5050
11.9%
44 3868
 
9.1%
23 3867
 
9.1%
61 2505
 
5.9%
72 2478
 
5.9%
22 1998
 
4.7%
62 1191
 
2.8%
53 896
 
2.1%
Other values (14) 3319
 
7.8%
ValueCountFrequency (%)
0 9798
23.2%
11 7
 
< 0.1%
21 28
 
0.1%
22 1998
 
4.7%
23 3867
 
9.1%
31 138
 
0.3%
32 865
 
2.0%
33 5050
11.9%
42 7337
17.3%
44 3868
 
9.1%
ValueCountFrequency (%)
81 169
 
0.4%
72 2478
5.9%
71 337
 
0.8%
62 1191
2.8%
61 2505
5.9%
56 672
 
1.6%
55 29
 
0.1%
54 267
 
0.6%
53 896
 
2.1%
52 84
 
0.2%
Distinct3868
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
Minimum1977-03-11 00:00:00
Maximum2073-10-17 00:00:00
2024-02-06T20:48:53.952453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:54.116916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5378
Minimum1974
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:54.272354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1991
Q11997
median2003
Q32006
95-th percentile2010
Maximum2014
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8605268
Coefficient of variation (CV)0.0029280121
Kurtosis0.16185187
Mean2001.5378
Median Absolute Deviation (MAD)4
Skewness-0.69419236
Sum84679059
Variance34.345775
MonotonicityNot monotonic
2024-02-06T20:48:54.437186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2004 4708
 
11.1%
2007 3777
 
8.9%
2006 3314
 
7.8%
2003 3293
 
7.8%
2005 2712
 
6.4%
1995 2552
 
6.0%
2000 2295
 
5.4%
1996 1739
 
4.1%
2002 1726
 
4.1%
2008 1723
 
4.1%
Other values (28) 14468
34.2%
ValueCountFrequency (%)
1974 4
 
< 0.1%
1977 5
 
< 0.1%
1979 21
 
< 0.1%
1980 56
0.1%
1981 13
 
< 0.1%
1982 78
0.2%
1983 80
0.2%
1984 79
0.2%
1985 134
0.3%
1986 77
0.2%
ValueCountFrequency (%)
2014 9
 
< 0.1%
2013 105
 
0.2%
2012 318
 
0.8%
2011 781
 
1.8%
2010 948
 
2.2%
2009 1158
 
2.7%
2008 1723
4.1%
2007 3777
8.9%
2006 3314
7.8%
2005 2712
6.4%

City
Text

Distinct2703
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:54.693108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length8.8926892
Min length3

Characters and Unicode

Total characters376223
Distinct characters65
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique827 ?
Unique (%)2.0%

Sample

1st rowPHOENIX
2nd rowMCALESTER
3rd rowHAWTHORNE
4th rowNASHVILLE
5th rowPOMONA
ValueCountFrequency (%)
city 1492
 
2.7%
san 1325
 
2.4%
houston 1224
 
2.3%
pittsburgh 958
 
1.8%
lake 812
 
1.5%
salt 716
 
1.3%
new 616
 
1.1%
philadelphia 614
 
1.1%
nashville 600
 
1.1%
pomona 584
 
1.1%
Other values (2347) 45399
83.5%
2024-02-06T20:48:55.102653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 353936
94.1%
Space Separator 12034
 
3.2%
Lowercase Letter 9453
 
2.5%
Other Punctuation 370
 
0.1%
Open Punctuation 262
 
0.1%
Close Punctuation 151
 
< 0.1%
Decimal Number 10
 
< 0.1%
Dash Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36799
 
10.4%
E 32773
 
9.3%
O 29514
 
8.3%
L 29423
 
8.3%
N 28734
 
8.1%
I 24008
 
6.8%
S 23939
 
6.8%
R 22162
 
6.3%
T 20421
 
5.8%
C 13141
 
3.7%
Other values (16) 93022
26.3%
Lowercase Letter
ValueCountFrequency (%)
o 1044
11.0%
e 998
10.6%
a 940
9.9%
n 940
9.9%
l 873
9.2%
r 813
8.6%
i 696
 
7.4%
s 567
 
6.0%
t 499
 
5.3%
u 258
 
2.7%
Other values (16) 1825
19.3%
Decimal Number
ValueCountFrequency (%)
6 2
20.0%
8 2
20.0%
5 2
20.0%
0 2
20.0%
2 2
20.0%
Other Punctuation
ValueCountFrequency (%)
. 271
73.2%
' 59
 
15.9%
, 40
 
10.8%
Space Separator
ValueCountFrequency (%)
12034
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363389
96.6%
Common 12834
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36799
 
10.1%
E 32773
 
9.0%
O 29514
 
8.1%
L 29423
 
8.1%
N 28734
 
7.9%
I 24008
 
6.6%
S 23939
 
6.6%
R 22162
 
6.1%
T 20421
 
5.6%
C 13141
 
3.6%
Other values (42) 102475
28.2%
Common
ValueCountFrequency (%)
12034
93.8%
. 271
 
2.1%
( 262
 
2.0%
) 151
 
1.2%
' 59
 
0.5%
, 40
 
0.3%
- 6
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
5 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

State
Text

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:55.265236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84614
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowOK
3rd rowNJ
4th rowTN
5th rowCA
ValueCountFrequency (%)
ca 6893
 
16.3%
tx 4095
 
9.7%
ny 2953
 
7.0%
pa 2849
 
6.7%
fl 1920
 
4.5%
oh 1229
 
2.9%
ut 1166
 
2.8%
tn 1147
 
2.7%
wa 1050
 
2.5%
mn 1004
 
2.4%
Other values (41) 18001
42.5%
2024-02-06T20:48:55.544831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84614
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 84614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%
Distinct51
Distinct (%)0.1%
Missing11
Missing (%)< 0.1%
Memory size661.0 KiB
2024-02-06T20:48:55.696577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84592
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowOK
3rd rowNJ
4th rowSD
5th rowCA
ValueCountFrequency (%)
ca 6476
15.3%
nc 3320
 
7.8%
il 2944
 
7.0%
oh 2785
 
6.6%
ri 2541
 
6.0%
tx 2457
 
5.8%
sd 2382
 
5.6%
ny 2197
 
5.2%
pa 1307
 
3.1%
ut 1133
 
2.7%
Other values (41) 14754
34.9%
2024-02-06T20:48:55.972417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 84592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

DisbursementGross
Real number (ℝ)

Distinct2694
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17935735
Minimum400000
Maximum6.296554 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:56.138867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum400000
5-th percentile1000000
Q13900000
median9992200
Q321800000
95-th percentile67570000
Maximum6.296554 × 108
Range6.292554 × 108
Interquartile range (IQR)17900000

Descriptive statistics

Standard deviation24872317
Coefficient of variation (CV)1.3867465
Kurtosis23.630558
Mean17935735
Median Absolute Deviation (MAD)7022200
Skewness3.5277948
Sum7.5880714 × 1011
Variance6.1863217 × 1014
MonotonicityNot monotonic
2024-02-06T20:48:56.308451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 2773
 
6.6%
5000000 2016
 
4.8%
2500000 1433
 
3.4%
500000 1298
 
3.1%
6000000 1041
 
2.5%
15000000 971
 
2.3%
8000000 955
 
2.3%
14500000 773
 
1.8%
1700000 729
 
1.7%
1000000 690
 
1.6%
Other values (2684) 29628
70.0%
ValueCountFrequency (%)
400000 246
 
0.6%
405300 2
 
< 0.1%
440000 1
 
< 0.1%
451000 1
 
< 0.1%
500000 1298
3.1%
500900 6
 
< 0.1%
505600 1
 
< 0.1%
509900 2
 
< 0.1%
510000 1
 
< 0.1%
528500 1
 
< 0.1%
ValueCountFrequency (%)
629655400 1
 
< 0.1%
325900000 3
 
< 0.1%
300000000 4
 
< 0.1%
287100000 1
 
< 0.1%
250000000 2
 
< 0.1%
229350000 7
 
< 0.1%
222525300 1
 
< 0.1%
200000000 57
0.1%
199900000 2
 
< 0.1%
199770000 3
 
< 0.1%

GrAppv
Real number (ℝ)

Distinct1425
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17395940
Minimum200000
Maximum3.5 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:56.475905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum200000
5-th percentile900000
Q13000000
median8500000
Q320250000
95-th percentile67000000
Maximum3.5 × 108
Range3.498 × 108
Interquartile range (IQR)17250000

Descriptive statistics

Standard deviation24726574
Coefficient of variation (CV)1.4213992
Kurtosis16.300384
Mean17395940
Median Absolute Deviation (MAD)6000000
Skewness3.2930646
Sum7.3597002 × 1011
Variance6.1140345 × 1014
MonotonicityNot monotonic
2024-02-06T20:48:56.817722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 3257
 
7.7%
5000000 2934
 
6.9%
2500000 2153
 
5.1%
500000 1426
 
3.4%
1000000 1330
 
3.1%
6000000 1088
 
2.6%
15000000 1051
 
2.5%
8000000 988
 
2.3%
1500000 905
 
2.1%
2000000 902
 
2.1%
Other values (1415) 26273
62.1%
ValueCountFrequency (%)
200000 2
 
< 0.1%
300000 15
 
< 0.1%
350000 3
 
< 0.1%
400000 244
 
0.6%
440000 1
 
< 0.1%
500000 1426
3.4%
510000 1
 
< 0.1%
530000 1
 
< 0.1%
540000 4
 
< 0.1%
550000 4
 
< 0.1%
ValueCountFrequency (%)
350000000 1
 
< 0.1%
347100000 3
 
< 0.1%
300000000 4
 
< 0.1%
287100000 1
 
< 0.1%
250000000 2
 
< 0.1%
236000000 2
 
< 0.1%
200000000 57
0.1%
199900000 2
 
< 0.1%
199770000 3
 
< 0.1%
199000000 3
 
< 0.1%

SBA_Appv
Real number (ℝ)

Distinct2005
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13760125
Minimum100000
Maximum3.471 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size661.0 KiB
2024-02-06T20:48:56.992248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile500000
Q12000000
median6400000
Q316125000
95-th percentile53959500
Maximum3.471 × 108
Range3.47 × 108
Interquartile range (IQR)14125000

Descriptive statistics

Standard deviation20556206
Coefficient of variation (CV)1.4938967
Kurtosis20.441211
Mean13760125
Median Absolute Deviation (MAD)5150000
Skewness3.542001
Sum5.8214963 × 1011
Variance4.2255759 × 1014
MonotonicityNot monotonic
2024-02-06T20:48:57.180649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500000 2382
 
5.6%
1250000 1705
 
4.0%
9000000 1023
 
2.4%
425000 985
 
2.3%
5000000 982
 
2.3%
500000 957
 
2.3%
11600000 757
 
1.8%
5100000 742
 
1.8%
8000000 729
 
1.7%
1360000 715
 
1.7%
Other values (1995) 31330
74.1%
ValueCountFrequency (%)
100000 2
 
< 0.1%
150000 15
 
< 0.1%
175000 3
 
< 0.1%
220000 1
 
< 0.1%
250000 394
0.9%
255000 1
 
< 0.1%
265000 1
 
< 0.1%
270000 4
 
< 0.1%
275000 4
 
< 0.1%
310000 2
 
< 0.1%
ValueCountFrequency (%)
347100000 3
 
< 0.1%
287100000 1
 
< 0.1%
270000000 4
 
< 0.1%
262500000 1
 
< 0.1%
225000000 2
 
< 0.1%
200000000 8
 
< 0.1%
199900000 2
 
< 0.1%
174400000 2
 
< 0.1%
160200000 2
 
< 0.1%
150000000 41
0.1%

UrbanRural
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.0 KiB
0
24037 
1
11759 
2
6511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Length

2024-02-06T20:48:57.346437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T20:48:57.471073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Interactions

2024-02-06T20:48:48.311180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:35.761098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.136471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.523397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.880641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.515891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.877681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.196607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.541465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.999934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.441647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:35.906014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.278588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.655719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.023139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.638745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.008215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.330762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.670685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.132784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.575655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.045180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.413765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.791325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.188390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.766031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.147005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.468400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.802975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.270150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.704492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.187161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.549889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.922430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.334019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.890862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.276135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.603254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.934950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.400915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.836170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.321948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.686978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.064956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.489969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.030636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.408101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.737532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.061341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.533140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.953988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.444886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.822574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.184909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.627831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.158227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.526804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.860723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.179715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.653102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:49.087924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.583070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.963844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.317770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:40.935087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.287996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.659441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.000288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.311124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.787428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:49.223395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.734522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.104749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.456204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.099426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.489358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.799702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.137146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.448219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:47.926270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:49.353135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:36.864197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.237187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.596055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.237032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.626785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:43.928921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.269277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.572634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.051159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:49.484143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:37.002311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:38.382775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:39.731565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:41.382132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:42.753397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:44.063308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:45.404956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:46.867950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-06T20:48:48.180900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-02-06T20:48:49.690551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-06T20:48:50.070554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
0163211.0001NN31-Jan-981022-Sep-062006PHOENIXAZSD8000000800000068000000
18461.04000N31-Oct-9316230-Jun-921992MCALESTEROKOK2870000028700000229600000
2242451.04900NN31-Aug-0114218-Apr-012001HAWTHORNENJNJ3198300300000015000001
323741.0000NN31-Aug-071336-Oct-032004NASHVILLETNSD2290000022900000229000000
418401.0000NN8-Jun-831017-Dec-992000POMONACACA5250000052500000393750000
56071.04100YN1-Apr-1204426-Nov-931994APLINGTONIAIA6999100700000035000000
63901.015100N8-Nov-111234-Jan-052005DALLASTXCA5000000500000025000000
78252.0001NC31-Jan-951021-Nov-012002HUDSONNHNH4140000041400000414000000
85762.0000NC31-Jan-9516111-Jan-951995WILLISTONNDND1125000011250000101250000
92511.0001NN30-Apr-071023-Mar-042004MESAAZAZ5000000500000025000002
TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
422975721.000960NN29-Jun-01108-Mar-961996SACRAMENTOCACO105000001050000089250000
4229882151.0110725900N30-Apr-0614415-May-032003DALLASTXTX5000000500000025000000
422991021.0010YN30-Jun-080728-Mar-002000CLARENCENYNY6700000067000000670000001
423008231.0001NN31-Dec-051429-Jul-032003NEW YORKNYIL1500000015000000135000000
423018201.01811YN10-Dec-961237-Jul-092009PITTSBURGHPAPA5000005000004250001
42302283141.0001NN31-Jan-98102-Mar-951995PHILADELPHIAPAPA8000000800000068000000
423035321.0000YN3-Apr-911426-Jun-072007LOS ANGELESCASD5000005000004250001
423045962.0001NN28-Feb-0314214-Mar-032003COLUMBUSOHOH6000000600000051000000
42305295181.0080NN10-Dec-9714223-Aug-891989CLOQUETMNMN2940000029400000220500000
423068441.0080NN31-Oct-8917212-Apr-112011SAN GABRIELCANC6750000675000050625000